The present invention relates to a method for valuating an object, and in particular to a method wherein an expert system determines a value from the features of the object for a rule, weights the value with a weight from [0, 1] and valuates the object based on the value. The present invention also relates to an expert system for valuating an object based on features of the object, with a rule editor for defining rules for the valuation with a respective weight from [0, 1] and a rule module for determining a value per rule from the features and a valuation module for valuing the object based on the values.
Persons skilled in the art designate computer program products as “expert systems”, which can be directly loaded into a random access memory of a computer, and which comprise software codes for performing the described functions, when the computer program product is executed on the computer. Synonymously, also the computer on which said computer program product is executed is designated with the same term.
Methods and expert systems as described supra are used in particular to valuate the likeliness of a fraud attempt for a request to perform a credit card transaction as an object of the valuation. The detection of fraudulent transactions either with credit cards or other electronic payment systems is an equally serious and complex problem. A conclusion if a transaction has a fraudulent background must be made under very tight time constraints. In order to accomplish this with a high level of reliability, an entire history of transaction data has to be considered, since each particular transaction to be valued does not include enough information for this purpose. Due to the enormous number of occurring transactions, detecting patterns of fraudulent transactions is a very difficult task, even for an expert. Selecting the correct rule base, so that as many fraudulent transactions are detected as possible, without putting regular transactions under suspicion erroneously, is a very complex task, even for experts. Therefore, an expert system should be capable to give indications of improvement possibilities of the decision criteria employed.
EP 1 081 655 A1, which is held by the applicant, discloses an expert system which uses fuzzy formulated rules for the valuation and which determines the weights for the rules through training with a neuronal network. Other generally known systems are exclusively based on neuronal networks, in which the rules can hardly be interpreted either. Therefore, it is desirable, when the decision basics of the system are described in a form that is understandable more easily, e.g. in the form of fuzzy formulated rules.
The expert system known from EP 1 081 655 A1 enables the user to predetermine the decision criteria used or to influence them. Though it comprises high reliability, thus a high likeliness for detecting fraud attempts with a low rate of wrong positive results, however, the adjustment of the rule weighting by neuronal networks is performed without a human operator being able to trace the particular reasons. The mathematical complexity, which is inherent to the expert system, thus prevents to a large extent that know-how of the user is considered in the adaption of the rule weights.
Bentley et al. disclose the development of complex fuzzy formulated rules for detecting fraudulent credit card transactions by means of an evolutionary algorithm in the “Fuzzy Darwinian Detection of credit card fraud” (conference proceedings of the 14th Annual Fall Symposium of the Korean Information Processing Society, October 2000). The rules are developed in a single execution of the evolutionary algorithm and used for fraud detection; a manual modification during operation is not possible. The operator also has no option to specify the rules himself. The described expert system, as matter of principle, cannot interact with the user in a useful manner. The results of the evolutionary algorithm are always taken over by the system unconditionally.
It would therefore be desirable and advantageous to provide an improved method for valuating an object and to provide an expert system to obviate prior art shortcomings and to simplify such expert systems by allowing the operator to interact with the expert system.